Analyste de données : Certificat professionnel en analyse de données

Aperçu

Data Based Decision Making, Data Analysis. Data Collection, Cleaning, Statistical Analysis, Visualisation, Privacy.

What you'll learn:

Business AnalysisData AnalysisMicrosoft ExcelData ScienceData Collection and AcquisitionData Cleaning and PreparationStatistical AnalysisData Interpretation and ReportingData VisualisationData Privacy and EthicsTools and Software for Data AnalysisCareer Development and Job Market TrendsData Analysis (Core Foundational Skills)Introduction to Business and Data Analysis (Supplementary Module)Hands-on Experience (Practical Application)Data-Based Decision Making (Strategic Application)Advanced Microsoft Excel Usage (Specialized Skill)SQL and SQL for Data Analysis (Database Skills)Specialization:

Data Analysis in Marketing (Supplementary Module)Specialization:

Sales & Service Data Analysis & Analytics (Supplementary Module)Specialization:

Data Quality, Management & Governance (Supplementary Module)Data Based Decision Making and Cost-Benefit Analysis (Supplementary Module) Welcome to Program:

Data Analyst:

Professional Certificate in Data Analysis by MTF InstituteCourse provided by MTF Institute of Management, Technology and FinanceMTF is the global educational and research institute with HQ at Lisbon, Portugal, focused on business & professional hybrid (on-campus and online) education at areas:

Business & Administration, Science & Technology, Banking & Finance. MTF R&D center focused on research activities at areas:

Artificial Intelligence, Machine Learning, Data Science, Big Data, WEB3, Blockchain, Cryptocurrency & Digital Assets, Metaverses, Digital Transformation, Fintech, Electronic Commerce, Internet of Things.

MTF is the official partner of:

IBM, Intel, Microsoft, member of the Portuguese Chamber of Commerce and Industry.MTF is present in 216 countries and has been chosen by more than 712 000 students.Course Authors:

Dr. Alex Amoroso is a seasoned professional with a rich background in academia and industry, specializing in research methodologies, strategy formulation, and product development.

With a Doctorate Degree from the School of Social Sciences and Politics in Lisbon, Portugal, where she was awarded distinction and honour for her exemplary research, Alex Amoroso brings a wealth of knowledge and expertise to the table.In addition to her doctoral studies, Ms. Amoroso has served as an invited teacher, delivering courses on to wide range of students from undergraduate level to business students of professional and executives courses.

Currently, at EIMT in Zurich, Switzerland, she lectures for doctoral students, offering advanced instruction in research design and methodologies, and in MTFInstitute Ms. Amoroso is leading Product Development academical domain.In synergy between academical and business experience, Ms.

Amoroso achieved high results in business career, leading R&D activities, product development, strategic development, market analysis activities in wide range of companies. She implemented the best market practices in industries from Banking and Finance, to PropTech, Consulting and Research, and Innovative Startups.Alex Amoroso's extensive scientific production includes numerous published articles in reputable journals, as well as oral presentations and posters at international conferences.

Her research findings have been presented at esteemed institutions such as the School of Political and Social Sciences and the Stressed Out Conference at UCL, among others.With a passion for interdisciplinary collaboration and a commitment to driving positive change, Alex Amoroso is dedicated to empowering learners and professionals for usage of cutting edge methodologies for achieving of excellence in global business world.Dr. Pedro Nunes has built a multifaceted career combining academia and practical business expertise.

His educational journey culminated with a Doctorate in Economic Analysis and Business Strategy with a cum laude mention from the University of Santiago de Compostela. Professionally, he has navigated through various sectors, including technology, international commerce, and consultancy, with roles ranging from business analyst to director.

Currently, Pedro serves as a Professor in several DBA programs, applying his extensive industry experience and academic insights to educate the next generation of professionals."Data Analyst:

Professional Certificate in Data Analysis" course is structured to provide a comprehensive learning experience, starting with foundational data analysis skills and progressing to specialized applications and advanced techniques. Here's a breakdown of the key sections:

Section:

Data Analysis (Core Foundational Skills)This section lays the groundwork for data analysis, covering essential concepts like data collection, cleaning, preparation, and exploratory data analysis (EDA).It delves into statistical analysis, data visualization, and predictive analytics.Crucially, it addresses data interpretation, reporting, privacy, and ethics, ensuring a well-rounded understanding.You'll also gain insights into the tools and software used in data analysis, portfolio building, and career development.Section:

Introduction to Business and Data Analysis (Supplementary Module)This module bridges the gap between data analysis and business application.It focuses on understanding business needs, defining problems, and applying data analysis techniques to solve business challenges.You'll explore data types and sources, business data analysis techniques, and data visualization for business insights.Case studies and discussions on the role of technology in business and data analysis are included.Section:

Hands-on Experience (Practical Application)This section emphasizes practical skills development through hands-on exercises using industry-standard tools.You'll gain experience with Excel, SQL, Python, R, and Tableau.Exercises focus on tasks like retrieving and analyzing data, handling missing data, conducting statistical analysis, and creating data visualizations.Section:

Data-Based Decision Making (Strategic Application)This section focuses on using data to inform decision-making processes.It covers various types of analytics (descriptive, diagnostic, predictive, and prescriptive) and how they contribute to data-driven decision-making.You'll learn about data-driven culture, tools and technologies, and real-world case studies.Section:

Advanced Microsoft Excel Usage (Specialized Skill)This module dives deep into advanced Excel functionalities, including advanced formulas, data analysis, visualization, data management, and automation with macros and VBA.The module also talks about AI powered excel with Gemini and Copilot.It includes numerous practical exercises to reinforce learning.Section :

SQL and SQL for Data Analysis (Database Skills)This section provides a comprehensive understanding of SQL, covering basic commands, data retrieval and manipulation, advanced queries, joins, subqueries, and data modification.It also covers query optimization, indexing, and advanced SQL features.The module concludes with a final project and assessment preparation.Section:

Specialization:

Data Analysis in Marketing (Supplementary Module)This module is focused on the application of data analysis within a marketing context.It covers marketing data types, sources, key metrics, analytical models, segmentation, predictive analytics, A/B testing, campaign analysis, ethical considerations, and building a data-driven marketing culture.Section:

Specialization:

Sales & Service Data Analysis & Analytics (Supplementary Module)This module is focused on the analysis of data within sales and service enviroments.It covers topics such as sales trends, pipeline analysis, conversion rate optimization, customer churn prediction, and using data to improve customer support efficiency.It also covers data sources, quality, and cleaning.Section:

Specialization:

Data Quality, Management & Governance (Supplementary Module)This module focuses on the principles and practices of data governance and management.It covers data governance frameworks, roles, responsibilities, data quality management, and the use of data governance tools and technology.Section:

Data Based Decision Making and Cost-Benefit Analysis (Supplementary Module)This module combines data-based decision-making with cost-benefit analysis.It covers how to gather, analyze, and interpret data for decision-making, and how to conduct cost-benefit analyses to evaluate potential initiatives.It also covers the tools and technologies used for data-based decision-making.Data analysis is the process of collecting, cleaning, and organizing data to uncover patterns, insights, and trends that can help individuals and organizations make informed decisions.

It involves examining raw data to find answers to specific questions, identify potential problems, or discover opportunities for improvement. Data analysts transform raw data into actionable insights to help organisations improve operations, strategies, and customer experiences.

Core skills include statistical analysis, critical thinking, data visualisation, and proficiency in tools like Excel, SQL, Python, and Tableau. Learning data analysis skills is crucial for career building in today's data-driven world, both for professional positions and managers of all levels.

Here's why:

For Professionals:

Increased Employability:

Data analysis skills are in high demand across various industries. Professionals with these skills are more likely to secure well-paying jobs and advance in their careers.

Improved Decision-Making:

Data analysis enables professionals to make informed decisions based on evidence and insights rather than relying on intuition or guesswork. Enhanced Problem-Solving:

Data analysis helps professionals identify the root causes of problems, develop effective solutions, and track the effectiveness of interventions.

Increased Efficiency and Productivity:

By automating tasks and identifying areas for improvement, data analysis can help professionals work more efficiently and increase their productivity. For Managers:

Strategic Planning:

Data analysis provides managers with the insights needed to develop effective strategies, set realistic goals, and track progress towards objectives.

Performance Management:

Managers can use data to monitor team performance, identify areas for improvement, and provide targeted feedback to employees. Risk Management:

Data analysis can help managers identify potential risks, assess their impact, and develop mitigation strategies.

Innovation and Growth:

By analyzing data on customer behavior, market trends, and competitor activities, managers can identify opportunities for innovation and growth. Learning data analysis skills is essential for professionals and managers of all levels who want to succeed in today's data-driven world.

These skills can help individuals make better decisions, solve problems more effectively, and contribute to the success of their organizations.

Programme

• Introduction à l'analyse des données
Vue d'ensemble de l'analyse des données
Importance des données dans les décisions d'affaires
Outils et technologies pour l'analyse des données
• Collecte et nettoyage des données
Sources et types de données
Méthodes de collecte des données
Techniques de nettoyage et préparation des données
• Analyse exploratoire des données (EDA)
Statistiques descriptives
Techniques de visualisation des données
Identification des motifs et tendances
• Analyse statistique
Bases des statistiques inférentielles
Test d'hypothèse
Analyse de régression
• Introduction aux outils d'analyse des données
Excel pour l'analyse des données
Introduction à Python et R
Utilisation de SQL pour la manipulation des données
• Visualisation des données
Principes de la visualisation efficace des données
Outils de visualisation : Tableau et Power BI
Création de tableaux de bord et de rapports
• Techniques avancées d'analyse des données
Analyse de séries temporelles
Analyse de regroupement
Détection d'anomalies
• Apprentissage automatique pour l'analyse des données
Bases de l'apprentissage automatique
Apprentissage supervisé vs non supervisé
Application de l'apprentissage automatique dans l'analyse des données
• Fondamentaux des Big Data
Introduction aux outils et concepts des Big Data
Travail avec de grands ensembles de données
Comprendre l'entreposage de données et les lacs de données
• Prise de décision basée sur les données
Interprétation des données pour des insights commerciaux
Développement de stratégies basées sur les données
Études de cas et applications réelles
• Projet de synthèse
Conception et mise en œuvre du projet
Présentation des résultats
Revue par les pairs et retour d'information
• Révision et certification
Résumé des concepts clés
Évaluation finale
Processus de certification et prochaines étapes

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Enseigné par

MTF Institute of Management, Technology and Finance

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Matières

Data Science